
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management

doi: 10.3390/app142311112
Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.
- Aalborg University Denmark
- Aalborg University Library (AUB) Aalborg Universitet Research Portal Denmark
- University of Tabriz Iran (Islamic Republic of)
- Aalborg University Library (AUB) Denmark
- Aalborg University Library (AUB) Denmark
Technology, QH301-705.5, QC1-999, Smart Grids, /dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure; name=SDG 9 - Industry, Innovation, and Infrastructure, Machine Learning, power systems, /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy, smart grids, energy management systems, /dk/atira/pure/sustainabledevelopmentgoals/partnerships; name=SDG 17 - Partnerships for the Goals, Biology (General), Renewable Energy Sources (RES), QD1-999, T, Physics, Artificial Intelligence (AI), Power Systems, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, machine learning, renewable energy sources (RESs), Energy Management Systems, TA1-2040
Technology, QH301-705.5, QC1-999, Smart Grids, /dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure; name=SDG 9 - Industry, Innovation, and Infrastructure, Machine Learning, power systems, /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy, smart grids, energy management systems, /dk/atira/pure/sustainabledevelopmentgoals/partnerships; name=SDG 17 - Partnerships for the Goals, Biology (General), Renewable Energy Sources (RES), QD1-999, T, Physics, Artificial Intelligence (AI), Power Systems, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, machine learning, renewable energy sources (RESs), Energy Management Systems, TA1-2040
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
